Developing virtual physiology of human tumor tissue for malignancy assessment

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cancer Research

Background:

• Compressive stresses are associated with tumor malignancy and can promote a malignant phenotype in cancer cells.
• Understanding how tissue heterogeneity affects these stresses is crucial for comprehending tumor progression.

Purpose of the Study:

• To investigate the influence of patient-specific tumor tissue heterogeneity on the stresses experienced by tissue components.
• To explore the relationship between tissue composition, configuration, and stress magnitudes in breast tumors.

Main Methods:

• Developed a unique image-based, physics-driven in silico model of human breast tumor tissue.
• Utilized Fourier transform infrared (FTIR) microscopy to image and classify six distinct breast tissue components.
• Analyzed stress distribution based on component configuration, neighborhood, and initial surface area.

Main Results:

• Heterogeneous tissues with small, disconnected malignant components exhibited higher compressive stresses.
• Stress magnitude was found to be dependent on the spatial arrangement and initial surface area of tissue components.
• The in silico model predicted stresses on pre-cancerous lesions capable of driving them to a lethal state.

Conclusions:

• Tumor tissue heterogeneity significantly impacts stress distribution and magnitude.
• The spatial configuration of malignant components plays a critical role in determining experienced stresses.
• In silico modeling provides valuable insights into the biomechanical drivers of cancer progression and malignancy.